Overview

Dataset statistics

Number of variables19
Number of observations346
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.2 KiB
Average record size in memory107.3 B

Variable types

Numeric10
Categorical6
Boolean3

Alerts

rental_date has a high cardinality: 167 distinct values High cardinality
df_index is highly correlated with rental_month and 1 other fieldsHigh correlation
rental_month is highly correlated with df_index and 1 other fieldsHigh correlation
temp is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with rental_monthHigh correlation
rental_month is highly correlated with df_indexHigh correlation
df_index is highly correlated with rental_month and 1 other fieldsHigh correlation
rental_hour is highly correlated with rental_yearHigh correlation
rental_day is highly correlated with rental_yearHigh correlation
rental_month is highly correlated with df_index and 1 other fieldsHigh correlation
rental_year is highly correlated with df_index and 3 other fieldsHigh correlation
dayofweek is highly correlated with working_day and 1 other fieldsHigh correlation
working_day is highly correlated with dayofweek and 1 other fieldsHigh correlation
peak is highly correlated with dayofweek and 1 other fieldsHigh correlation
df_index is highly correlated with rental_month and 3 other fieldsHigh correlation
rental_hour is highly correlated with peak and 1 other fieldsHigh correlation
rental_day is highly correlated with temp and 1 other fieldsHigh correlation
rental_month is highly correlated with df_index and 5 other fieldsHigh correlation
rental_year is highly correlated with df_index and 1 other fieldsHigh correlation
dayofweek_n is highly correlated with dayofweek and 1 other fieldsHigh correlation
dayofweek is highly correlated with dayofweek_n and 1 other fieldsHigh correlation
working_day is highly correlated with rental_month and 4 other fieldsHigh correlation
season is highly correlated with df_index and 3 other fieldsHigh correlation
peak is highly correlated with rental_hour and 1 other fieldsHigh correlation
timesofday is highly correlated with rental_hourHigh correlation
temp is highly correlated with df_index and 4 other fieldsHigh correlation
count_day is highly correlated with rental_day and 2 other fieldsHigh correlation
df_index has unique values Unique
dayofweek_n has 32 (9.2%) zeros Zeros

Reproduction

Analysis started2022-04-12 18:40:09.759801
Analysis finished2022-04-12 18:40:21.990051
Duration12.23 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2039.089595
Minimum12
Maximum6675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:22.132546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile134.25
Q1671.25
median1810
Q33226.5
95-th percentile4790.25
Maximum6675
Range6663
Interquartile range (IQR)2555.25

Descriptive statistics

Standard deviation1539.584256
Coefficient of variation (CV)0.7550351194
Kurtosis-0.603021373
Mean2039.089595
Median Absolute Deviation (MAD)1207.5
Skewness0.5892219283
Sum705525
Variance2370319.682
MonotonicityStrictly increasing
2022-04-12T19:40:22.267938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121
 
0.3%
25681
 
0.3%
26641
 
0.3%
26541
 
0.3%
26351
 
0.3%
26331
 
0.3%
26301
 
0.3%
26111
 
0.3%
26101
 
0.3%
25871
 
0.3%
Other values (336)336
97.1%
ValueCountFrequency (%)
121
0.3%
291
0.3%
421
0.3%
591
0.3%
841
0.3%
931
0.3%
951
0.3%
961
0.3%
971
0.3%
991
0.3%
ValueCountFrequency (%)
66751
0.3%
64941
0.3%
64731
0.3%
61951
0.3%
53251
0.3%
51041
0.3%
49961
0.3%
49621
0.3%
49611
0.3%
49241
0.3%

rental_date
Categorical

HIGH CARDINALITY

Distinct167
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2021-04-24
 
7
2021-04-25
 
7
2021-03-07
 
7
2021-03-17
 
7
2021-04-03
 
7
Other values (162)
311 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)24.0%

Sample

1st row2021-03-01
2nd row2021-03-02
3rd row2021-03-03
4th row2021-03-04
5th row2021-03-05

Common Values

ValueCountFrequency (%)
2021-04-247
 
2.0%
2021-04-257
 
2.0%
2021-03-077
 
2.0%
2021-03-177
 
2.0%
2021-04-037
 
2.0%
2021-04-046
 
1.7%
2021-04-176
 
1.7%
2021-06-295
 
1.4%
2021-03-275
 
1.4%
2021-04-105
 
1.4%
Other values (157)284
82.1%

Length

2022-04-12T19:40:22.400522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-04-247
 
2.0%
2021-03-077
 
2.0%
2021-03-177
 
2.0%
2021-04-037
 
2.0%
2021-04-257
 
2.0%
2021-04-046
 
1.7%
2021-04-176
 
1.7%
2021-04-025
 
1.4%
2021-07-235
 
1.4%
2021-03-065
 
1.4%
Other values (157)284
82.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rental_hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.58959538
Minimum0
Maximum23
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:22.513207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q111
median13
Q316
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.146529049
Coefficient of variation (CV)0.2315395684
Kurtosis0.168789465
Mean13.58959538
Median Absolute Deviation (MAD)2
Skewness0.1107479614
Sum4702
Variance9.900645053
MonotonicityNot monotonic
2022-04-12T19:40:22.603073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1252
15.0%
1146
13.3%
1341
11.8%
1830
8.7%
1530
8.7%
1027
7.8%
1727
7.8%
1426
7.5%
1624
6.9%
913
 
3.8%
Other values (7)30
8.7%
ValueCountFrequency (%)
01
 
0.3%
810
 
2.9%
913
 
3.8%
1027
7.8%
1146
13.3%
1252
15.0%
1341
11.8%
1426
7.5%
1530
8.7%
1624
6.9%
ValueCountFrequency (%)
231
 
0.3%
221
 
0.3%
211
 
0.3%
205
 
1.4%
1911
 
3.2%
1830
8.7%
1727
7.8%
1624
6.9%
1530
8.7%
1426
7.5%

rental_day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.10115607
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:22.699617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q322
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.525062716
Coefficient of variation (CV)0.5645304688
Kurtosis-1.146158321
Mean15.10115607
Median Absolute Deviation (MAD)7
Skewness-0.002382658239
Sum5225
Variance72.67669431
MonotonicityNot monotonic
2022-04-12T19:40:22.814374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1720
 
5.8%
1620
 
5.8%
318
 
5.2%
815
 
4.3%
215
 
4.3%
1314
 
4.0%
414
 
4.0%
1913
 
3.8%
1112
 
3.5%
2212
 
3.5%
Other values (20)193
55.8%
ValueCountFrequency (%)
110
2.9%
215
4.3%
318
5.2%
414
4.0%
56
 
1.7%
69
2.6%
711
3.2%
815
4.3%
95
 
1.4%
1012
3.5%
ValueCountFrequency (%)
308
2.3%
2911
3.2%
2810
2.9%
2711
3.2%
269
2.6%
2511
3.2%
2412
3.5%
2311
3.2%
2212
3.5%
219
2.6%

rental_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.953757225
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:22.921048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.519063615
Coefficient of variation (CV)0.4231048595
Kurtosis-0.9594381869
Mean5.953757225
Median Absolute Deviation (MAD)2
Skewness0.4300097308
Sum2060
Variance6.345681495
MonotonicityNot monotonic
2022-04-12T19:40:23.031157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
367
19.4%
466
19.1%
743
12.4%
641
11.8%
529
8.4%
1029
8.4%
828
8.1%
924
 
6.9%
1114
 
4.0%
23
 
0.9%
Other values (2)2
 
0.6%
ValueCountFrequency (%)
11
 
0.3%
23
 
0.9%
367
19.4%
466
19.1%
529
8.4%
641
11.8%
743
12.4%
828
8.1%
924
 
6.9%
1029
8.4%
ValueCountFrequency (%)
121
 
0.3%
1114
 
4.0%
1029
8.4%
924
 
6.9%
828
8.1%
743
12.4%
641
11.8%
529
8.4%
466
19.1%
367
19.4%

rental_year
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2021
342 
2022
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021342
98.8%
20224
 
1.2%

Length

2022-04-12T19:40:23.179047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-12T19:40:23.540345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2021342
98.8%
20224
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

holiday
Boolean

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size598.0 B
False
317 
True
 
29
ValueCountFrequency (%)
False317
91.6%
True29
 
8.4%
2022-04-12T19:40:23.587683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

dayofweek_n
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.502890173
Minimum0
Maximum6
Zeros32
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:23.656900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.938546527
Coefficient of variation (CV)0.5534134475
Kurtosis-1.099432829
Mean3.502890173
Median Absolute Deviation (MAD)1
Skewness-0.3969883327
Sum1212
Variance3.757962637
MonotonicityNot monotonic
2022-04-12T19:40:23.752155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
584
24.3%
656
16.2%
454
15.6%
141
11.8%
341
11.8%
238
11.0%
032
 
9.2%
ValueCountFrequency (%)
032
 
9.2%
141
11.8%
238
11.0%
341
11.8%
454
15.6%
584
24.3%
656
16.2%
ValueCountFrequency (%)
656
16.2%
584
24.3%
454
15.6%
341
11.8%
238
11.0%
141
11.8%
032
 
9.2%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size830.0 B
Saturday
84 
Sunday
56 
Friday
54 
Thursday
41 
Tuesday
41 
Other values (2)
70 

Length

Max length9
Median length7
Mean length7.170520231
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowTuesday
3rd rowWednesday
4th rowThursday
5th rowFriday

Common Values

ValueCountFrequency (%)
Saturday84
24.3%
Sunday56
16.2%
Friday54
15.6%
Thursday41
11.8%
Tuesday41
11.8%
Wednesday38
11.0%
Monday32
 
9.2%

Length

2022-04-12T19:40:23.859025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-12T19:40:23.945303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
saturday84
24.3%
sunday56
16.2%
friday54
15.6%
thursday41
11.8%
tuesday41
11.8%
wednesday38
11.0%
monday32
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size598.0 B
True
188 
False
158 
ValueCountFrequency (%)
True188
54.3%
False158
45.7%
2022-04-12T19:40:24.029154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

season
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size678.0 B
Spring
138 
Summer
101 
Winter
54 
Autumn
53 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring138
39.9%
Summer101
29.2%
Winter54
 
15.6%
Autumn53
 
15.3%

Length

2022-04-12T19:40:24.126004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-12T19:40:24.243197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
spring138
39.9%
summer101
29.2%
winter54
 
15.6%
autumn53
 
15.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size598.0 B
False
250 
True
96 
ValueCountFrequency (%)
False250
72.3%
True96
 
27.7%
2022-04-12T19:40:24.303382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

timesofday
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size678.0 B
Afternoon
200 
Morning
96 
Evening
48 
Night
 
2

Length

Max length9
Median length9
Mean length8.144508671
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowEvening
3rd rowAfternoon
4th rowMorning
5th rowAfternoon

Common Values

ValueCountFrequency (%)
Afternoon200
57.8%
Morning96
27.7%
Evening48
 
13.9%
Night2
 
0.6%

Length

2022-04-12T19:40:24.435791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-12T19:40:24.579519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon200
57.8%
morning96
27.7%
evening48
 
13.9%
night2
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct163
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.48815029
Minimum3.3
Maximum26.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:24.723039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile5.575
Q110.1
median13.2
Q317.2
95-th percentile22.175
Maximum26.3
Range23
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation4.902079244
Coefficient of variation (CV)0.3634359893
Kurtosis-0.644429132
Mean13.48815029
Median Absolute Deviation (MAD)3.7
Skewness0.160060788
Sum4666.9
Variance24.03038092
MonotonicityNot monotonic
2022-04-12T19:40:24.854218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.87
 
2.0%
10.17
 
2.0%
11.46
 
1.7%
11.36
 
1.7%
8.86
 
1.7%
16.35
 
1.4%
13.65
 
1.4%
10.35
 
1.4%
17.75
 
1.4%
16.95
 
1.4%
Other values (153)289
83.5%
ValueCountFrequency (%)
3.32
0.6%
3.51
 
0.3%
4.11
 
0.3%
4.21
 
0.3%
4.51
 
0.3%
4.62
0.6%
4.71
 
0.3%
4.82
0.6%
5.13
0.9%
5.21
 
0.3%
ValueCountFrequency (%)
26.32
0.6%
24.31
0.3%
23.71
0.3%
23.51
0.3%
23.42
0.6%
23.31
0.3%
23.21
0.3%
22.91
0.3%
22.71
0.3%
22.61
0.3%

rhum
Real number (ℝ≥0)

Distinct58
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.36705202
Minimum24
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:24.976837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile47.25
Q160
median69
Q376
95-th percentile88.75
Maximum98
Range74
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.48377317
Coefficient of variation (CV)0.1825992608
Kurtosis0.06790344061
Mean68.36705202
Median Absolute Deviation (MAD)8
Skewness-0.1725588781
Sum23655
Variance155.8445924
MonotonicityNot monotonic
2022-04-12T19:40:25.130824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6914
 
4.0%
6713
 
3.8%
7213
 
3.8%
7313
 
3.8%
7113
 
3.8%
7913
 
3.8%
6313
 
3.8%
7412
 
3.5%
6412
 
3.5%
6811
 
3.2%
Other values (48)219
63.3%
ValueCountFrequency (%)
241
 
0.3%
361
 
0.3%
381
 
0.3%
391
 
0.3%
402
 
0.6%
412
 
0.6%
432
 
0.6%
445
1.4%
452
 
0.6%
471
 
0.3%
ValueCountFrequency (%)
982
 
0.6%
971
 
0.3%
962
 
0.6%
943
0.9%
913
0.9%
901
 
0.3%
896
1.7%
884
1.2%
876
1.7%
865
1.4%

wdsp
Real number (ℝ≥0)

Distinct20
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.043352601
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:25.256248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median9
Q311
95-th percentile15.75
Maximum22
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.56811553
Coefficient of variation (CV)0.3945567189
Kurtosis0.6473410997
Mean9.043352601
Median Absolute Deviation (MAD)3
Skewness0.7274960821
Sum3129
Variance12.73144844
MonotonicityNot monotonic
2022-04-12T19:40:25.365362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
649
14.2%
737
10.7%
936
10.4%
834
9.8%
1034
9.8%
1228
8.1%
1126
7.5%
524
6.9%
416
 
4.6%
1316
 
4.6%
Other values (10)46
13.3%
ValueCountFrequency (%)
23
 
0.9%
35
 
1.4%
416
 
4.6%
524
6.9%
649
14.2%
737
10.7%
834
9.8%
936
10.4%
1034
9.8%
1126
7.5%
ValueCountFrequency (%)
222
 
0.6%
211
 
0.3%
201
 
0.3%
184
 
1.2%
172
 
0.6%
168
 
2.3%
156
 
1.7%
1414
4.0%
1316
4.6%
1228
8.1%

rain_type
Categorical

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size686.0 B
no rain
341 
drizzle
 
2
moderate rain
 
2
light rain
 
1

Length

Max length13
Median length7
Mean length7.043352601
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowno rain
2nd rowno rain
3rd rowno rain
4th rowno rain
5th rowno rain

Common Values

ValueCountFrequency (%)
no rain341
98.6%
drizzle2
 
0.6%
moderate rain2
 
0.6%
light rain1
 
0.3%

Length

2022-04-12T19:40:25.486035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-12T19:40:25.555921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
rain344
49.9%
no341
49.4%
drizzle2
 
0.3%
moderate2
 
0.3%
light1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count_hour
Real number (ℝ≥0)

Distinct13
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.86416185
Minimum12
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:25.628618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q112
median13
Q315
95-th percentile18.75
Maximum26
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.262879791
Coefficient of variation (CV)0.1632179295
Kurtosis4.932132174
Mean13.86416185
Median Absolute Deviation (MAD)1
Skewness1.947849596
Sum4797
Variance5.120624948
MonotonicityNot monotonic
2022-04-12T19:40:25.724867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12112
32.4%
1391
26.3%
1450
14.5%
1532
 
9.2%
1619
 
5.5%
1716
 
4.6%
188
 
2.3%
198
 
2.3%
205
 
1.4%
242
 
0.6%
Other values (3)3
 
0.9%
ValueCountFrequency (%)
12112
32.4%
1391
26.3%
1450
14.5%
1532
 
9.2%
1619
 
5.5%
1716
 
4.6%
188
 
2.3%
198
 
2.3%
205
 
1.4%
211
 
0.3%
ValueCountFrequency (%)
261
 
0.3%
242
 
0.6%
231
 
0.3%
211
 
0.3%
205
 
1.4%
198
 
2.3%
188
 
2.3%
1716
4.6%
1619
5.5%
1532
9.2%

count_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.7254335
Minimum57
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-12T19:40:25.841278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile81.25
Q1107.25
median123
Q3134
95-th percentile163
Maximum171
Range114
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation23.43062014
Coefficient of variation (CV)0.1924874651
Kurtosis-0.288604986
Mean121.7254335
Median Absolute Deviation (MAD)13.5
Skewness0.01770175112
Sum42117
Variance548.99396
MonotonicityNot monotonic
2022-04-12T19:40:25.965226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13019
 
5.5%
13716
 
4.6%
12311
 
3.2%
12611
 
3.2%
11511
 
3.2%
13411
 
3.2%
11710
 
2.9%
11110
 
2.9%
12410
 
2.9%
1079
 
2.6%
Other values (64)228
65.9%
ValueCountFrequency (%)
571
 
0.3%
701
 
0.3%
711
 
0.3%
721
 
0.3%
734
1.2%
741
 
0.3%
761
 
0.3%
781
 
0.3%
791
 
0.3%
801
 
0.3%
ValueCountFrequency (%)
1717
2.0%
1687
2.0%
1637
2.0%
1623
0.9%
1577
2.0%
1557
2.0%
1545
1.4%
1536
1.7%
1514
1.2%
1485
1.4%

Interactions

2022-04-12T19:40:20.288318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:10.561875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.753554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.865722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.956276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.861752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.813933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.688160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.821850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.989425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.395713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:10.674591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.905073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.959664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.054144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.956101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.906363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.778956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.935232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.185197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.510772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:10.771359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.077807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.048905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.139291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.068038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.987928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.862668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.038780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.334543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.633474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:10.943332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.229472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.141158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.234481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.168155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.079392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.959140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.145628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.482991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.746164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.065799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.322499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.233839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.323733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.257692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.165207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.050872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.251469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.607227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.859720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.180948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.408563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.325027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.409907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.349854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.257145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.146395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.357318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.743554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.966419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.293697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.489105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.414285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.491686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.431646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.337540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.234883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.457592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.855746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:21.114134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.416145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.583007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.661341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.588080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.526891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.426943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.330568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.567320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:19.968694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:21.269368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.527387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.678606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.775545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.685382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.631945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.518661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.614030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.695634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.081923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:21.381750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:11.649676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:12.771636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:13.870147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:14.774624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:15.728026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:16.606135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:17.731176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:18.842104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-12T19:40:20.187202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-12T19:40:26.083479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-12T19:40:26.235201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-12T19:40:26.378036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-12T19:40:26.514232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-12T19:40:26.661622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-12T19:40:21.608998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-12T19:40:21.885553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexrental_daterental_hourrental_dayrental_monthrental_yearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdaytemprhumwdsprain_typecount_hourcount_day
0122021-03-0118132021False0MondayTrueWinterTrueEvening5.3918no rain14107
1292021-03-0218232021False1TuesdayTrueWinterTrueEvening4.2886no rain14105
2422021-03-0312332021False2WednesdayTrueWinterFalseAfternoon5.1878no rain14107
3592021-03-0411432021False3ThursdayTrueWinterFalseMorning4.5819no rain1493
4842021-03-0516532021False4FridayTrueWinterTrueAfternoon4.6596no rain12120
5932021-03-069632021False5SaturdayFalseWinterFalseMorning4.1642no rain13137
6952021-03-0611632021False5SaturdayFalseWinterFalseMorning5.5577no rain14137
7962021-03-0612632021False5SaturdayFalseWinterFalseAfternoon6.7575no rain13137
8972021-03-0613632021False5SaturdayFalseWinterFalseAfternoon6.8567no rain12137
9992021-03-0615632021False5SaturdayFalseWinterFalseAfternoon6.5589no rain14137

Last rows

df_indexrental_daterental_hourrental_dayrental_monthrental_yearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdaytemprhumwdsprain_typecount_hourcount_day
33649242021-11-131413112021False5SaturdayFalseAutumnFalseAfternoon11.6895no rain13115
33749612021-11-151115112021False0MondayTrueAutumnFalseMorning10.8875no rain1295
33849622021-11-151215112021False0MondayTrueAutumnFalseAfternoon10.8877no rain1295
33949962021-11-17817112021False2WednesdayTrueAutumnTrueMorning7.7809no rain1493
34051042021-11-221322112021False0MondayTrueAutumnFalseAfternoon7.7766no rain1273
34153252021-12-04164122021False5SaturdayFalseAutumnFalseAfternoon3.58517no rain1292
34261952022-01-20182012022False3ThursdayTrueWinterTrueEvening5.1794no rain1281
34364732022-02-0318322022False3ThursdayTrueWinterTrueEvening11.07312no rain12118
34464942022-02-0417422022False4FridayTrueWinterTrueAfternoon3.38514no rain1385
34566752022-02-13111322022False6SundayFalseWinterFalseMorning8.8894no rain1273